Well-Being Global Index: Assays of multivariate statistical approaches Maria do Carmo Botelho 1 (maria.botelho@iscte-iul.pt) Rosário Mauritti 1 (rosario.mauritti@iscte-iul.pt) Nuno Nunes 1 (nuno.nunes@iscte-iul.pt) Daniela Craveiro 2 (daniela.craveiro@iscte-iul.pt) Paulo Neto 3 (neto@uevora.pt) 1 ISCTE-University Institute of Lisbon, CIES-IUL 2 ISCTE-University Institute of Lisbon, CIS-IUL, CIES-IUL 3 Évora University, CICS.NOVA UEVORA, UMPP , CIES-IUL
Introduction This research is part of a larger project about well-being inequalities in Europe, named “Territories of Inequality and Well - being” , sponsored by Francisco Manuel dos Santos Foundation. This investigation is based in a multidimensional understanding of social inequality and well-being, explored within and across OECD European countries. To measure well-being, our orientation is the OECD Better Life Initiative and the quality of life results, available by Eurostat. The OECD’s How's Life (2011, 2013, 2015b, 2017a), allows the measure and the knowledge of the Europeans well-being, based on a multi- dimensional framework, following the “Beyond the GDP” agenda.
Project Goals Understanding everyday life in Europe Analysing territorial inequalities and their relationships with the well- Construction of a being of system of indicators Europeans to improve data availability, Place-based and analysis on the monitoring of the multidimensional impacts of European relationships public policies and interventions 3
Aims The main goal of this presentation is to discuss the statistical construction of indicators of well-being , based on microdata bases. The specific objectives for this presentation are: 1. Present previous work about structural configurations of well-being inequalities across individuals and countries, based on microdata from the European Social Survey (ESS), 2016; 2. Discuss data limitations and the undertaken statistical analyses; 3. Identify and select appropriate indicators for two dimensions of well-being: environment and housing, using European Quality of Life Survey (EQLS) microdata, also from 2016; 4. Create two composite dimensions, reducing multidimensionality and construct each dimension with multivariate statistical analysis.
1. Structural configurations of well-being inequalities across individuals and countries The use of European Social Survey (ESS) microdata Based on OCDE well-being framework, we identify several key indicators for measuring nine dimensions of well-being. Only two were omitted: Housing and Education. The used questions were mostly Likert type items. Well-being indicators were normalised, using de min-max method (OECD, 2016), resulting values from zero to 10 in all dimensions. For dimensions with two or more indicators, the arithmetic mean was calculated.
1. Structural configurations of well-being inequalities across individuals and countries The use of European Social Survey (ESS) microdata Social inequalities multidimensionality - Distributional inequalities: Income and Education - Categorical inequalities: Gender and Social Class (1) (1) ACM typology (Costa et al., 2002), Socio-occupation indicator constructed on the basis of a cross matrix of class locations formed by the ISCO08 occupations * employment status Entrepreneurs and executives (EE), Professionals and managers (PM), Self-employed (SE), Routine employees (RE) and Industrial workers (IW).
1. Structural configurations of well-being inequalities across individuals and countries Well-being Global Volume (WBGV) Well-being profiles among Europeans Well-being profiles among countries Cluster analysis Multiple Regression Analysis Association measures 8
Well-being profiles among europeans Elite (38.0%; N=7331) WBGV=7.1 Lowest well-being (17.9%; N= 3452) WBGV=4.9 9
Well-being profiles among europeans Insecure well-being (13.6%; N=2637) WBGV=5.7 Individualist well-being (30.5%; N= 5898) WBGV=5.9 10
Well-being profiles among countries Nordic high-rank (WBGV=6.9) IC, NO, SW Central Europe medium-rank Southern Europe (WBGV=6.4) medium-rank AT, BE, CH, DE, FI, GB, NL (WBGV=6.2) FR, PT, SP 11
Well-being profiles among countries Eastern Europe low-rank (WBGV=5.8) EE, HU, IE, PL, SI Social disengagement low-rank (WBGV=5.5) CZ, IT, LI 12
2. Discuss data limitations and the undertaken statistical analyses Few indicators in each well-being dimensions The min-max methodology is not the best for these indicators The use of the arithmetic mean for the global volume can be debatable Difficulty in measuring well-being related with housing and environment The exclusive use of subjective indicators
3. Environment and housing The use of European Quality of Life Survey (EQLS) microdata For each dimension, a systematization of the indicators used by OECD and Eurostat was made, and also a comparison to ESS indicators, when possible. Analysis of different microdata bases, available in the UK Data Service, possible due to the support of the INGRIG-2 program, with the visit to the Cathie Marsh Institute, University of Manchester. The analyzed databases were British Social Attitudes, Community Life Survey, Continuous Household Survey, EU-SILC, Understanding Society, European Value Study and the European Quality of Life Survey (EQLS) . Identification and selection of indicators for the two dimensions of well-being (individual level and country level).
Environment Study Dimension Indicators Measures Source Urban population exposure to air pollution by particulate European Environment Agency matter (PM10) (EEA), yearly Quant Var: Micrograms per cubic meter 8.1 Pollution (including noise) Perception of pollution, Measures and grime or other EU-SILC , yearly; in the future, this environmental problems variable would be collected within the sources of Eurostat 8. Natural 3-year rolling module on housing Quality and living Noise from neighbours or indicators used by of Life environment from the street 8.2 Access to OECD, Eurostat green and Satisfaction with recreational EU-SILC 2013 ad hoc module on recreational and green areas well-being spaces 8.3 Landscape EU-SILC 2013 ad hoc module on Comparison with Satisfaction with the living and built well-being; next: EU-SILC 3-year environment environment rolling module on housing the chosen ESS indicators Population weighted average OECD calculations based on data of annual concentrations of from the Global Burden of Disease particulate matters less than assessment (Brauer, M. et al. (2016) Air pollution 2.5 microns in diameter "Ambient Air Pollution Exposure (PM2.5) in the air Estimation for the Global Burden of Environmental OECD Quant Var: Micrograms per Disease 2013." Environmental Science quality cubic meter & Technology 50 (1), Pages 79-88). In the city or area where you Satisfaction with live, are you satisfied or Gallup World Poll water quality dissatisfied with the quality of water? D24-How worried are you Environmental about climate change? D21- D25 other questions about ESS Climate change concern 1-Not at all worried, 5- climate change* Extremely worried
Environment Indicator Measure Values/categories Source Neighbourhood problems: Noise noise Neighbourhood problems: Air quality Selected 1 - Major problems air quality 2 - Moderate problems indicators Neighbourhood problems: 3 - No problems Litter or rubbish litter or rubbish on the street EQLS of the individuals Neighbourhood problems: European Traffic heavy traffic Quality of 1 - Very difficult Life Survey How easy or difficult is your 2 - Rather difficult Green areas access to access to recreational 3 - Rather easy or green areas 4 - Very easy 2013 EU-SILC module Living Satisfaction with the living High, medium, low on subjective WB environment* environment Eurostat Satisfaction with In the city or area where you live, are you satisfied or Gallup World Poll water quality* dissatisfied with the quality of water? Urban population exposure to air pollution by European Environment Pollution** particulate matter (PM10) Agency (EEA): Quant Var: Micrograms per cubic meter Eurostat *Value by country **Objective measure; Value by country
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